A two-dimensional approach to relationship conflict: Meta-analytic findings.
Bibliographic record
Abstract
This meta-analysis of 64 studies (5,071 couples) used a metacoding system to categorize observed couple conflict behaviors into categories differing in terms of valence (positive to negative) and intensity (high to low) and resulting in five behavioral categories: hostility, distress, withdrawal, problem solving, and intimacy. Aggregate effect sizes indicated that women were somewhat more likely to display hostility, distress, and intimacy during conflict, whereas men were somewhat more likely to display withdrawal and problem solving. Gender differences were of a small magnitude. For both men and women, hostility was robustly associated with lower relationship satisfaction (medium effect), distress and withdrawal were somewhat associated (small effect), and intimacy and problem solving were both closely associated with relationship satisfaction (medium effect). Effect sizes were moderated in several cases by study characteristics including year of publication, developmental period of the sample, recruitment design, duration of observed conflict, method used to induce conflict, and type of coding system used. Findings from this meta-analysis suggest that high-intensity conflict behaviors of both a positive and negative nature are important correlates of relationship satisfaction and underscore the relatively small gender differences in many conflict behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".